from sklearn_benchmarks.reporting.hp_match import HPMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HPMatchReporting("sklearnex", config="config.yml")
reporting.run()

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.013	0.0	NaN	6.086	0.0	-1	1	NaN	NaN	0.066	0.000	0.198	0.198	See	See
5	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.013	0.0	NaN	6.031	0.0	-1	5	NaN	1.000	0.010	0.001	1.287	1.289	See	See
10	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	6.581	0.0	1	100	NaN	0.875	0.452	0.017	0.027	0.027	See	See
15	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	6.563	0.0	-1	100	NaN	NaN	0.051	0.000	0.240	0.240	See	See
20	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	6.462	0.0	1	5	NaN	1.000	0.001	0.000	19.653	19.983	See	See
25	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	6.629	0.0	1	1	NaN	0.975	0.140	0.007	0.086	0.086	See	See
30	KNeighborsClassifier_brute_force	False	fit	100000	100000	2	0.005	0.0	NaN	0.314	0.0	-1	1	NaN	NaN	0.009	0.000	0.546	0.546	See	See
35	KNeighborsClassifier_brute_force	False	fit	100000	100000	2	0.005	0.0	NaN	0.307	0.0	-1	5	NaN	1.000	0.001	0.000	8.582	8.715	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	19.102	0.163	NaN	0.000	0.019	-1	1	0.663	0.687	0.408	0.014	46.801	46.828	See	See
2	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	2.377	0.140	NaN	0.000	0.002	-1	1	0.663	1.000	0.011	0.001	222.016	222.797	See	See
3	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.349	0.009	NaN	0.000	0.349	-1	1	1.000	NaN	0.047	0.000	7.417	7.417	See	See
4	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.029	0.005	NaN	0.000	0.029	-1	1	1.000	0.742	0.403	0.010	0.073	0.073	See	See
6	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	19.082	0.330	NaN	0.000	0.019	-1	5	0.757	NaN	0.047	0.000	407.156	407.156	See	See
7	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	3.241	0.112	NaN	0.000	0.003	-1	5	0.757	0.875	0.452	0.011	7.175	7.177	See	See
8	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.357	0.013	NaN	0.000	0.357	-1	5	1.000	0.000	0.010	0.000	34.787	34.796	See	See
9	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.028	0.003	NaN	0.000	0.028	-1	5	1.000	NaN	0.047	0.000	0.596	0.596	See	See
11	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	19.609	0.133	NaN	0.000	0.020	1	100	0.882	0.000	0.011	0.001	1830.076	1836.086	See	See
12	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	2.259	0.027	NaN	0.000	0.002	1	100	0.882	NaN	0.047	0.000	47.671	47.671	See	See
13	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.353	0.014	NaN	0.000	0.353	1	100	1.000	0.742	0.413	0.016	0.856	0.856	See	See
14	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.022	0.001	NaN	0.000	0.022	1	100	1.000	1.000	0.011	0.000	2.091	2.092	See	See
16	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	19.001	0.017	NaN	0.000	0.019	-1	100	0.882	0.687	0.411	0.006	46.244	46.249	See	See
17	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	3.267	0.064	NaN	0.000	0.003	-1	100	0.882	1.000	0.011	0.001	285.778	288.182	See	See
18	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.346	0.007	NaN	0.000	0.346	-1	100	1.000	NaN	0.009	0.000	36.690	36.690	See	See
19	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.027	0.002	NaN	0.000	0.027	-1	100	1.000	0.967	0.085	0.004	0.322	0.322	See	See
21	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	19.101	0.002	NaN	0.000	0.019	1	5	0.757	NaN	0.009	0.000	2050.197	2050.197	See	See
22	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	2.247	0.030	NaN	0.000	0.002	1	5	0.757	0.974	0.085	0.003	26.320	26.342	See	See
23	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.344	0.010	NaN	0.000	0.344	1	5	1.000	1.000	0.001	0.000	566.971	576.097	See	See
24	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.022	0.002	NaN	0.000	0.022	1	5	1.000	NaN	0.009	0.000	2.405	2.405	See	See
26	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	18.964	0.045	NaN	0.000	0.019	1	1	0.663	1.000	0.001	0.000	26690.947	27033.448	See	See
27	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	1.355	0.011	NaN	0.001	0.001	1	1	0.663	NaN	0.009	0.000	145.402	145.402	See	See
28	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.356	0.014	NaN	0.000	0.356	1	1	1.000	0.975	0.135	0.003	2.629	2.629	See	See
29	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.024	0.004	NaN	0.000	0.024	1	1	1.000	1.000	0.001	0.000	29.857	30.652	See	See
31	KNeighborsClassifier_brute_force	True	predict	100000	1000	2	4.418	0.034	NaN	0.000	0.004	-1	1	0.896	0.974	0.088	0.005	50.148	50.245	See	See
32	KNeighborsClassifier_brute_force	False	predict	100000	1000	2	2.111	0.080	NaN	0.000	0.002	-1	1	0.896	1.000	0.001	0.000	3507.942	3560.929	See	See
33	KNeighborsClassifier_brute_force	True	predict	100000	1	2	0.296	0.011	NaN	0.000	0.296	-1	1	1.000	NaN	0.009	0.000	31.937	31.937	See	See
34	KNeighborsClassifier_brute_force	False	predict	100000	1	2	0.010	0.004	NaN	0.000	0.010	-1	1	1.000	0.967	0.085	0.003	0.117	0.117	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	3.137	0.0	NaN	0.026	0.0	-1	1	NaN	NaN	1.049	0.000	2.991	2.991	See	See
5	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	6.807	0.0	NaN	0.012	0.0	-1	5	NaN	1.000	0.001	0.000	13299.932	14646.113	See	See
10	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	5.975	0.0	NaN	0.013	0.0	1	100	NaN	0.940	0.771	0.021	7.751	7.754	See	See
15	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	5.766	0.0	NaN	0.014	0.0	-1	100	NaN	NaN	0.893	0.000	6.454	6.454	See	See
20	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	5.998	0.0	NaN	0.013	0.0	1	5	NaN	1.000	0.000	0.000	34586.904	37822.689	See	See
25	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	5.900	0.0	NaN	0.014	0.0	1	1	NaN	0.917	0.006	0.000	1033.676	1037.188	See	See
30	KNeighborsClassifier_kd_tree	False	fit	1000	1000	2	0.005	0.0	NaN	0.004	0.0	-1	1	NaN	NaN	0.006	0.000	0.793	0.793	See	See
35	KNeighborsClassifier_kd_tree	False	fit	1000	1000	2	0.001	0.0	NaN	0.015	0.0	-1	5	NaN	1.000	0.000	0.000	6.140	6.698	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	145.746	0.000	NaN	0.000	0.146	-1	1	0.929	0.910	0.141	0.004	1035.623	1035.990	See	See
2	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	0.547	0.016	NaN	0.000	0.001	-1	1	0.929	1.000	0.000	0.000	1532.024	1647.683	See	See
3	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	3.204	0.078	NaN	0.000	3.204	-1	1	1.000	NaN	0.934	0.000	3.430	3.430	See	See
4	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.004	0.002	NaN	0.000	0.004	-1	1	1.000	0.941	0.254	0.007	0.016	0.016	See	See
6	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	144.760	0.000	NaN	0.000	0.145	-1	5	0.946	NaN	0.971	0.000	149.066	149.066	See	See
7	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	1.029	0.021	NaN	0.000	0.001	-1	5	0.946	0.940	0.774	0.031	1.330	1.331	See	See
8	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	3.143	0.119	NaN	0.000	3.143	-1	5	1.000	1.000	0.001	0.000	3035.327	3277.622	See	See
9	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.004	0.000	NaN	0.000	0.004	-1	5	1.000	NaN	0.942	0.000	0.004	0.004	See	See
11	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	162.295	0.000	NaN	0.000	0.162	1	100	0.951	1.000	0.001	0.000	173403.731	180529.464	See	See
12	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	6.248	0.051	NaN	0.000	0.006	1	100	0.951	NaN	0.889	0.000	7.031	7.031	See	See
13	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	3.161	0.086	NaN	0.000	3.161	1	100	1.000	0.941	0.251	0.013	12.616	12.632	See	See
14	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.004	0.001	NaN	0.000	0.004	1	100	1.000	1.000	0.000	0.000	7.829	8.304	See	See
16	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	170.348	0.000	NaN	0.000	0.170	-1	100	0.951	0.910	0.138	0.007	1236.356	1237.789	See	See
17	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	3.288	0.031	NaN	0.000	0.003	-1	100	0.951	1.000	0.000	0.000	9910.403	10574.995	See	See
18	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	3.188	0.104	NaN	0.000	3.188	-1	100	1.000	NaN	0.001	0.000	2866.436	2866.436	See	See
19	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.008	0.005	NaN	0.000	0.008	-1	100	1.000	0.879	0.001	0.000	12.822	12.944	See	See
21	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	168.187	0.000	NaN	0.000	0.168	1	5	0.946	NaN	0.003	0.000	58270.533	58270.533	See	See
22	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	1.812	0.026	NaN	0.000	0.002	1	5	0.946	0.905	0.001	0.000	1677.624	1694.281	See	See
23	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	3.141	0.078	NaN	0.000	3.141	1	5	1.000	1.000	0.000	0.000	17758.425	19375.858	See	See
24	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.002	0.000	NaN	0.000	0.002	1	5	1.000	NaN	0.009	0.000	0.201	0.201	See	See
26	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	170.030	0.000	NaN	0.000	0.170	1	1	0.929	1.000	0.000	0.000	905181.576	978362.657	See	See
27	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	0.966	0.021	NaN	0.000	0.001	1	1	0.929	NaN	0.006	0.000	160.839	160.839	See	See
28	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	3.224	0.175	NaN	0.000	3.224	1	1	1.000	0.917	0.008	0.004	422.424	468.689	See	See
29	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.001	0.000	NaN	0.000	0.001	1	1	1.000	1.000	0.000	0.000	6.657	7.238	See	See
31	KNeighborsClassifier_kd_tree	True	predict	1000	1000	2	0.050	0.014	NaN	0.000	0.000	-1	1	0.891	0.905	0.001	0.000	56.242	56.535	See	See
32	KNeighborsClassifier_kd_tree	False	predict	1000	1000	2	0.032	0.005	NaN	0.001	0.000	-1	1	0.891	1.000	0.000	0.000	175.953	191.107	See	See
33	KNeighborsClassifier_kd_tree	True	predict	1000	1	2	0.006	0.000	NaN	0.000	0.006	-1	1	1.000	NaN	0.004	0.000	1.407	1.407	See	See
34	KNeighborsClassifier_kd_tree	False	predict	1000	1	2	0.003	0.001	NaN	0.000	0.003	-1	1	1.000	0.879	0.001	0.001	2.793	3.381	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	False	fit	1000000	1000000	2	0.826	0.0	30	0.019	0.0	random	NaN	30	NaN	0.323	0.0	2.556	2.556	See	See
3	KMeans_tall	False	fit	1000000	1000000	2	0.712	0.0	30	0.022	0.0	k-means++	NaN	30	NaN	0.361	0.0	1.972	1.972	See	See
6	KMeans_tall	False	fit	1000000	1000000	100	7.406	0.0	30	0.108	0.0	random	NaN	30	NaN	4.058	0.0	1.825	1.825	See	See
9	KMeans_tall	False	fit	1000000	1000000	100	7.054	0.0	30	0.113	0.0	k-means++	NaN	30	NaN	3.998	0.0	1.764	1.764	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	False	predict	1000000	1000	2	0.002	0.001	30	0.008	0.000	random	0.000	30	0.001	0.000	0.0	8.073	8.918	See	See
2	KMeans_tall	False	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	random	1.000	30	1.000	0.000	0.0	8.500	9.189	See	See
4	KMeans_tall	False	predict	1000000	1000	2	0.002	0.000	30	0.008	0.000	k-means++	0.001	30	0.001	0.000	0.0	8.192	8.752	See	See
5	KMeans_tall	False	predict	1000000	1	2	0.002	0.002	30	0.000	0.002	k-means++	1.000	30	1.000	0.000	0.0	12.481	13.615	See	See
7	KMeans_tall	False	predict	1000000	1000	100	0.002	0.000	30	0.414	0.000	random	0.002	30	0.002	0.001	0.0	3.497	3.796	See	See
8	KMeans_tall	False	predict	1000000	1	100	0.002	0.000	30	0.000	0.002	random	1.000	30	1.000	0.000	0.0	6.189	7.842	See	See
10	KMeans_tall	False	predict	1000000	1000	100	0.002	0.000	30	0.376	0.000	k-means++	0.002	30	0.002	0.000	0.0	6.132	6.417	See	See
11	KMeans_tall	False	predict	1000000	1	100	0.002	0.000	30	0.000	0.002	k-means++	1.000	30	1.000	0.000	0.0	8.544	9.205	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	False	fit	10000	10000	2	0.095	0.0	20	0.002	0.0	random	NaN	20	NaN	0.062	0.0	1.540	1.540	See	See
3	KMeans_short	False	fit	10000	10000	2	0.295	0.0	20	0.001	0.0	k-means++	NaN	20	NaN	0.124	0.0	2.368	2.368	See	See
6	KMeans_short	False	fit	10000	10000	100	0.347	0.0	20	0.023	0.0	random	NaN	20	NaN	0.265	0.0	1.312	1.312	See	See
9	KMeans_short	False	fit	10000	10000	100	1.109	0.0	20	0.007	0.0	k-means++	NaN	20	NaN	0.636	0.0	1.744	1.744	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	False	predict	10000	1000	2	0.003	0.002	20	0.005	0.000	random	0.002	20	0.001	0.001	0.001	2.844	4.166	See	See
2	KMeans_short	False	predict	10000	1	2	0.002	0.000	20	0.000	0.002	random	1.000	20	1.000	0.000	0.000	7.708	8.375	See	See
4	KMeans_short	False	predict	10000	1000	2	0.002	0.000	20	0.007	0.000	k-means++	0.002	20	0.001	0.001	0.000	3.272	3.302	See	See
5	KMeans_short	False	predict	10000	1	2	0.002	0.000	20	0.000	0.002	k-means++	1.000	20	1.000	0.000	0.000	8.299	9.145	See	See
7	KMeans_short	False	predict	10000	1000	100	0.004	0.001	20	0.191	0.000	random	0.321	20	0.311	0.002	0.001	2.323	2.471	See	See
8	KMeans_short	False	predict	10000	1	100	0.002	0.001	20	0.000	0.002	random	1.000	20	1.000	0.000	0.000	10.340	11.071	See	See
10	KMeans_short	False	predict	10000	1000	100	0.003	0.000	20	0.239	0.000	k-means++	0.381	20	0.286	0.002	0.000	2.095	2.126	See	See
11	KMeans_short	False	predict	10000	1	100	0.002	0.001	20	0.000	0.002	k-means++	1.000	20	1.000	0.000	0.000	6.788	7.336	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	False	fit	1000000	1000000	100	16.575	0.0	[20]	0.048	0.000	NaN	NaN	NaN	NaN	NaN	2.904	0.0	5.707	5.707	See	See
3	LogisticRegression	False	fit	1000	1000	10000	4.631	0.0	[26]	0.017	0.005	NaN	NaN	NaN	NaN	NaN	1.165	0.0	3.974	3.974	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	False	predict	1000000	1000	100	0.001	0.0	[20]	1.461	0.0	NaN	NaN	NaN	NaN	0.56	0.001	0.0	0.755	0.804	See	See
2	LogisticRegression	False	predict	1000000	1	100	0.000	0.0	[20]	0.008	0.0	NaN	NaN	NaN	NaN	1.00	0.000	0.0	0.340	0.360	See	See
4	LogisticRegression	False	predict	1000	100	10000	0.002	0.0	[26]	3.204	0.0	NaN	NaN	NaN	NaN	0.35	0.005	0.0	0.553	0.554	See	See
5	LogisticRegression	False	predict	1000	1	10000	0.000	0.0	[26]	0.588	0.0	NaN	NaN	NaN	NaN	0.00	0.001	0.0	0.149	0.152	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	False	fit	1000	1000	10000	0.310	0.0	NaN	0.258	0.0	NaN	NaN	NaN	0.301	0.0	1.031	1.031	See	See
3	Ridge	False	fit	1000000	1000000	100	1.713	0.0	NaN	0.467	0.0	NaN	NaN	NaN	1.179	0.0	1.453	1.453	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	False	predict	1000	1000	10000	0.011	0.001	NaN	7.060	0.0	NaN	NaN	0.083	0.02	0.004	0.561	0.570	See	See
2	Ridge	False	predict	1000	1	10000	0.000	0.000	NaN	0.748	0.0	NaN	NaN	NaN	0.00	0.000	0.634	0.686	See	See
4	Ridge	False	predict	1000000	1000	100	0.000	0.000	NaN	4.118	0.0	NaN	NaN	1.000	0.00	0.000	0.619	0.648	See	See
5	Ridge	False	predict	1000000	1	100	0.000	0.000	NaN	0.007	0.0	NaN	NaN	NaN	0.00	0.000	0.780	0.860	See	See